Multiresolution Colour Texture Synthesis

A novel fast algorithm for realistic colour texture synthesis is introduced. The algorithm starts with spectral factorization of an input colour texture image using the Karhunen-Loeve expansion. Single orthogonal monospectral components are further decomposed into a multi-resolution grid and each resolution data are independently mod-eled by their dedicated Gaussian Markov random eld model (GMRF). We estimate an optimal contextual neighbourhood and parameters for each GMRF. Finally single synthesized monospectral texture pyramids are collapsed into the ne resolution images and using the inverse Karhunen-Loeve transformation we obtain the required colour texture. The beneet of the multigrid approach is the replacement of a large neighbourhood GMRF model with several simpler GMRF models which are easy to synthesize and wider application area of these multigrid models capable of reproducing realistic textures for enhancing realism in virtual reality systems.